As computer networks expand swiftly, it is essential to guarantee that the information system upholds confidentiality, reliability, and accessibility. Intrusion detection systems (IDSs) are essential instruments for overseeing and safeguarding networks. Current intrusion detection systems have two significant issues: the detection rate for zero-day attacks is low, and the prevalence of false-positive alarms is considerable. We need detection systems that can learn to correctly identify intrusions if we are going to solve these problems. Numerous researchers have proposed hybrid solutions grounded in machine learning techniques. These strategies exploit the vulnerabilities of detection techniques to capitalize on their shortcomings. This study evaluates proposed model and shows that it is generating the optimized result with 98.97% accuracy on NSL-KDD dataset. The study concentrates on hybrid detecting systems. This analysis juxtaposes our suggested methodologies with established techniques on the NSL-KDD, UNSW-NB15, KDD-99, and CIC-IDS 2018 dataset, underscoring the constraints in the evolution of hybrid intrusion detection systems and accentuating the necessity for additional research in this domain.